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数据挖掘领域中类别不平衡数据分类属于热门研究课题.在传统分类算法中,由于存在一定程度的偏向性,使得少数类的分类效果欠佳.基于此,提出一种多层级联式少数类聚类高精度数据挖掘算法.该算法基于聚类进行欠采样,在多数类样本上进行聚类并提取聚类质心,得到数目等同少数类样本的聚类质心,之后和所有少数类样例一起构建新平衡训练集.为杜绝少数类样本数量过少导致训练集过小而影响分类精度,利用SMOTE过采样结合聚类欠采样,在平衡训练集上通过K均值聚类和C4.5决策树算法相级联的分类方式来优化分类决策的边界.实验表明,该算法在处理类别不平衡数据分类问题方面具备一定的优势.“,”In the field of machine learning and data excavating, the classification of imbalanced data is a hot research topic. In the traditional classification algorithm, the existence of a certain degree of bias makes the classification of a small number poor. To solve this problem, a new algorithm for clustering high precision data excavating with multi-class cascade is proposed. Based on clustering, the algorithm constructs a new balanced training set, which is based on SMOTE. And K means clustering is used to cluster the training samples into K clusters, and the C4.5 algorithm mean clustering algorithm is used to optimize the classification decision by means of K clustering. The algorithm is based on C4.5 algorithm. Experiments show that this algorithm has certain advantages in dealing with the problem of data classification.